Statistical Methods for Molecular Evolution

分子进化的统计方法

• 批准号: RGPIN-2019-04287
• 负责人: Susko, Edward
• 金额: $ 1.46万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759138
• 关键词: Statistical; Methods; Molecular; Evolution

Statistical methods for inference about evolution from aligned molecular sequence data will be the focus of this research. In addition, working with colleagues in biology and biochemistry, methods will be applied to better understand the evolution of early single-celled organisms and how processes of selection work in pathogens. While statistical theory and methods will be developed with evolutionary inference questions in mind, the statistical methods and theory will be more broadly applicable to other areas of science. Four project areas will be targeted: 1. Tree Testing: Understanding what the evolutionary relationships are between organisms is an important step in understanding their biology. We will derive new statistical methods that yield confidence sets of evolutionary trees that are likely to include the true relationship and provide information about where uncertainty about evolutionary relationships lies.  2. Positive Selection: This occurs when organisms adapt to changing environmental conditions. Detecting positive selection is of importance, for instance, in understanding how pathogens that infect humans can evolve resistance. Current methodology sometimes gives biologically unreasonable estimates of the strength of selection and can give unreliable testing results. We are developing methods that are less susceptible to the sparse-data issues that cause such problems. Another area of interest is developing models that jointly models changes in the functions that an organism's genes performs and changes in DNA. Such models will give a better understanding of which locations in a gene are important for particular biological functions.  3. Protein Evolution Models: Evolution of the proteins that perform the functions of organisms is a complex process. Understanding such evolutionary processes is crucial to inference about the relationships between organisms and of interest in itself. We will develop more realistic models that accommodate the frequently observed phenomenon that evolutionary processes vary over proteins and positions within proteins. 4. Model Selection: An important task in modeling evolution is to select classes of models that adjust for important processes but without becoming so complex that data is insufficient for their estimation. We will develop methods that penalize excess complexity and test model performance by considering key performance measures on validation data.

从分子序列数据中推断进化的统计方法将是本研究的重点。此外，与生物学和生物化学的同事合作，将应用方法来更好地理解早期单细胞生物的进化以及选择过程如何在病原体中起作用。虽然统计理论和方法将与进化推理问题一起发展，但统计方法和理论将更广泛地适用于其他科学领域。将针对四个项目领域：1。树测试：了解生物体之间的进化关系是了解其生物学的重要一步。我们将推导出新的统计方法，产生可能包含真实关系的进化树的置信度集，并提供关于进化关系不确定性所在的信息。2. 正向选择：当生物体适应不断变化的环境条件时，这种情况就会发生。检测阳性选择是很重要的，例如，在了解感染人类的病原体如何进化出耐药性方面。目前的方法有时对选择的强度给出生物学上不合理的估计，并可能给出不可靠的测试结果。我们正在开发的方法不太容易受到导致此类问题的稀疏数据问题的影响。另一个感兴趣的领域是开发模型，将生物体基因功能的变化和DNA的变化联合起来。这样的模型将使我们更好地了解基因中的哪些位置对特定的生物功能是重要的。3. 蛋白质进化模型：执行生物体功能的蛋白质的进化是一个复杂的过程。理解这样的进化过程对于推断生物体之间的关系和自身的兴趣是至关重要的。我们将开发更现实的模型，以适应经常观察到的蛋白质和蛋白质内部位置的进化过程变化的现象。4. 模型选择：建模进化中的一个重要任务是选择适合重要过程的模型类别，但不会变得过于复杂，以至于数据不足以对其进行估计。我们将开发一些方法，通过考虑验证数据上的关键性能度量来惩罚过度的复杂性和测试模型性能。